混合模型框架下的模型，如潜变量增长混合模型（latent growth mixture modeling，LGMM）或潜类别增长分析（latent class growth analysis，LCGA），因估算过程中涉及多个决策过程，导致潜变量轨迹分析结果的报告呈现多样性。为解决这一问题，指南制订小组按照系统化的制订流程，通过 4 轮德尔菲法调查，遵循专家小组意见，提出了各领域报告潜变量轨迹分析结果时需采用统一的标准，最终确定了报告轨迹研究结果必要的关键条目，发布了潜变量轨迹研究报告规范（guidelines for reporting on latent trajectory studies，GRoLTS），并利用 GRoLTS 评价了 38 篇使用 LGMM 或 LCGA 研究创伤后应激轨迹的论文的报告情况。
In medical research, latent subgroups often emerge with characteristics or trends distinct from the general population, yet identifying them directly remain challenging. The latent variable mixture modeling, grounded in the idea that a population consists of a limited mixture of subgroups, assigns latent categories to individuals based on posterior probabilities. This model is suitable for both cross-sectional and longitudinal datasets. Approaching from a statistical perspective, this paper thoroughly explicates the foundational principles of four prevalent methods within the latent variable mixture modeling realm, outlining the essential modeling workflow. By integrating insights from previous cases and real-world data, we review the rational applications of these methods. The latent variable mixture modeling stands as a flexible classification tool for identifying and analyzing latent categories within research populations, further facilitating the in-depth exploration of predictors influencing these latent categories and their consequent effects on outcome variables.
Objective To investigate the development trajectories of kinesiophobia and their influencing factors in patients after total hip arthroplasty (THA). Methods Patients after THA from three tertiary hospitals in Wuhan from February to June 2023 were selected by convenience sampling method. The general situation questionnaire, Tampa Scale for Kinesiophobia, Self-Efficacy for Exercise Scale (SEE), Groningen Orthopaedic Social Support Scale, Generalized Anxiety Disorder, Patient Health Questionnaire, and Visual Analogue Scale (VAS) were distributed 1-2 d after surgery (T1), which were used again 1 week (T2), 1 month (T3), and 3 months (T4) after surgery, to evaluate the level of kinesiophobia and the physical and psychological conditions of the patients. The latent category growth model was used to classify the kinesiophobia trajectories of patients after THA, and the influencing factors of different categories of kinesiophobia trajectories were analyzed. Results A total of 263 patients after THA were included. The kinesiophobia trajectories of patients after THA were divided into four potential categories, including 29 cases in the C1 high kinesiophobia persistent group, 41 cases in the C2 medium kinesiophobia improvement group, 131 cases in the C3 low kinesiophobia improvement group, and 62 cases in the C4 no kinesiophobia group. Multicategorical logistic regression analysis showed that compared to the C4 no kinesiophobia group, the influencing factors for the kinesiophobia trajectory in THA patients to develop into the C1 high kinesiophobia persistent group were age [odds ratio (OR)=1.081, 95% confidence interval (CI) (1.025, 1.140)], chronic comorbidities [OR=6.471, 95%CI (1.831, 22.872)], the average SEE score at T1-T4 time points [OR=0.867, 95%CI (0.808, 0.931)], and the average VAS score at T1-T4 time points [OR=7.981, 95%CI (1.718, 37.074)], the influencing factors for the kinesiophobia trajectory to develop into the C2 medium kinesiophobia improvement group were age [OR=1.049, 95%CI (1.010, 1.089)], education level [OR=0.244, 95%CI (0.085, 0.703)], and the average VAS score at T1-T4 time points [OR=8.357, 95%CI (2.300, 30.368)], and the influencing factors for the kinesiophobia trajectory to develop into the C3 low kinesiophobia improvement group were the average SEE score [OR=0.871, 95%CI (0.825, 0.920)] and the average VAS score at T1-T4 time points [OR=4.167, 95%CI (1.544, 11.245)] . Conclusion Kinesiophobia in patients after THA presents different trajectories, and nurses should pay attention to the assessment and intervention of kinesiophobia in patients with advanced age, low education level, chronic diseases, low exercise self-efficacy, and high pain level.